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We improve the centrality definition in impact parameter in PACIAE model responding the fact reported by the ALICE, ATLAS, and CMS collaborations that the maximum impact parameter in heavy ion collisions should be extended to 20 fm. Meanwhile the PACIAE program is updated to a new version of PACIAE 2.2.2 with convenience of studying the elementary nuclear collisions, proton-nucleus collisions, and the nucleus-nucleus collisions in one unified program version. The new impact parameter definition together with the optical Glauber model calculated impact parameter bin, N part , and N coll in proton-nucleus and nucleus-nucleus collisions at relativistic energies are consistent with the improved MC-Glauber model ones within the error bar. The charged-particle pseudorapidity and the transverse momentum distributions in Pb-Pb collisions at s NN = 5.02 TeV simulated by PACIAE 2.2.2 well reproduce the ALICE experimental data. Program Title: PACIAE version 2.2.2 CPC Library link to program files: https://doi.org/10.17632/w3g68dj4d9.4 Licensing provisions: CC By 4.0 Programming language: FORTRAN Journal reference of previous version: Comput. Phys. Commun. 224 (2018) 417 Does the new version supersede the previous version?: Yes Reasons for the new version: Recently ALICE, ATLAS and CMS collaborations reported that the maximum impact parameter b m a x should be extended to 20 fm in the nuclear-nuclear collisions at relativistic energies. The impact parameter formula in PACIAE model has to be improved correspondingly. Meanwhile the PACIAE model is updated to PACIAE 2.2.2 with the convenience of studying the elementary nuclear collisions, proton-nucleus collisions, and the nucleus-nucleus collisions in one unified program version. Summary of revisions: The impact parameter b in PACIAE model is calculated by geometrical model of b = c × b m a x , where c refers to the centrality percentile and b m a x is assumed to be b m a x = R A + R B + f × d. In above equation R A (R B) is the radius of nuclear A (B), d = 0.546 fm describes the tail of the nuclear density profile. Originally, the coefficient f is set to be equal to 2 and 1 for nucleus-nucleus and proton-nucleus collisions, respectively. Now they are assumed to be equal to 4 and 2, respectively. Meanwhile, the PACIAE model is updated to the version of PACIAE 2.2.2 with the convenience of studying the elementary nuclear collisions, proton-nucleus collisions, and the nucleus-nucleus collisions in a unified program version. Nature of problem: The ALICE, ATLAS and CMS collaborations reported that the maximum impact parameter, b m a x , in heavy-ion collisions at relativistic energies should be extended to 20 fm where the interaction is really approaching to zero. The impact parameter centrality determination in heavy-ion collisions at relativistic energies has to be revised accordingly in the PACIAE model. Solution method: A new f coefficient sets in the impact parameter formula in PACIAE model, sequentially the new b bin corresponding to a given centrality percentile bin is introduced in the new version of PACIAE 2.2.2. [ABSTRACT FROM AUTHOR]

The parton and hadron cascade model PACIAE 2.2 (cf. Yan et al. (2018) [11]) has been upgraded to the new issue of PACIAE 2.2.1 with the chiral magnetic effect (CME). The charge correlation parameter, γ s s and γ o s , which is typically used to "detect" the CME, is calculated by PACIAE 2.2.1 in Au+Au and Pb+Pb collisions, and the result is consistent with the STAR and ALICE data. As an example, PACIAE 2.2.1 model is employed to mimic the pseudorapidity distribution (d N c h / d η) and transverse momentum spectra (p T) of charged particles in non-central Au+Au collisions at RHIC energy and Pb+Pb collisions at LHC energy. The chiral magnetic effect is found to partly affect the final-state basic observables in relativistic nuclear collisions. By this new issue a more realistic dynamical evolution of relativistic nuclear collisions can be depicted. Program Title: PACIAE version 2.2.1 CPC Library link to program files: https://doi.org/10.17632/w3g68dj4d9.3 Code Ocean capsule: https://codeocean.com/capsule/3695338 Licensing provisions: CC By 4.0 Programming language: FORTRAN 77 or GFORTRAN Journal reference of previous version: Comput. Phys. Comm. 224 (2018) 417 Does the new version supersede the previous version?: No. Reasons for the new version: In order that PACIAE 2.2.1 model is now able to simulate the CME-related physics and a more realistic dynamical evolution scenario in the relativistic nucleus-nucleus collisions. Summary of revisions: In PACIAE 2.2.1 model the chiral magnetic effect is introduced. The CME-induced charge separation mechanism [7] into the initial partonic states is artificially added by switching p y values for a fraction f of the downward moving partons with those of the corresponding upward moving antipartons. Nature of problem: PACIAE is based on PYTHIA, where it has no any mechanisms to generate the chiral magnetic effect (CME). The CME is a novel transport phenomenon arising from the interaction between quantum anomalies and strong magnetic fields in the ultra-relativistic nuclear collisions. Charge separation is an important consequence of the CME. In order to depict the CME, the corresponding upgrade about introducing a global charge separation scheme into the initial partonic states is highly required. Solution method: The global CME-induced charge separation mechanism [7] into the initial partonic states is introduced by switching p y values for a fraction f of the downward moving partons with those of the corresponding upward moving antipartons. Additional comments including restrictions and unusual features: PACIAE 2.2.1 has three versions of PACIAE 2.2.1a, 2.2.1b, and 2.2.1c. PACIAE 2.2.1a is for the elementary collision (here no upgraded from PACIAE 2.2a). PACIAE 2.2.1b and PACIAE 2.2.1c are for the nucleus-nucleus collision of A + A with input file of u s u. d a t. PACIAE 2.2.1b and PACIAE 2.2.1c are similar in the physical contents but are different in the topological structure [8]. • Using the attached input file of u s u x. d a t to run 1000 events for the s = 200 GeV Non Single Diffractive pp collision by PACIAE 2.2.1a takes ≈0.5 min. • Using the attached input file of u s u. d a t to run 10 events for the 35%-45% non-central Au+Au collisions at s N N = 200 GeV by PACIAE 2.2.1b takes ≈6 min. • Using the attached input file of u s u. d a t to run 10 events for the 35%-45% non-central Au+Au collisions at s N N = 200 GeV by PACIAE 2.2.1c takes ≈8 min. [ABSTRACT FROM AUTHOR]

Dimensional sentiment analysis has received considerable attention because it can represent affective states as continuous numerical values on multiple dimensions such as valence (positive–negative) and arousal (excited–calm). Compared to the categorical approach, which represents affective states as several discrete classes (e.g., positive and negative), the dimensional approach can provide more fine-grained (real-valued) sentiment analysis. Traditional approaches to predicting dimensional sentiment scores typically treat each dimension independently without consideration of relations between dimensions. In fact, different dimensions may correlate with each other. For example, expressions with a higher valence score usually have a higher arousal score, And higher irony expressions usually have a lower valence score. Such relations between dimensions are useful for dimension score prediction. To this end, this study proposes a multi-dimensional relation model to incorporate relations between dimensions into deep neural networks for dimension score prediction. The proposed method has two modes: internal and external. The internal mode incorporates the relations between dimensions into sentence representations before prediction, whereas the external mode builds a linear regression model that can capture the relations between dimensions to refine the predicted scores after prediction. To evaluate the proposed method, we created a Chinese three-dimensional corpus with valence-arousal-irony (VAI) ratings. Experiments using various neural network architectures demonstrate that the proposed multi-dimensional relation model outperformed those that treat each dimension independently. In addition, the internal mode outperformed the external mode, and a combination of the two modes achieved the best performance. [ABSTRACT FROM AUTHOR]

• Two spread models are proposed from the deterministic and random aspects based on the substitution dynamic system and the theory of branching processes. • The long-term behaviors of the spread rates are found in both deterministic and random models. • The transition of two spread patterns during the pandemic period can be described. • Our methodology provides a way to compare two spread patterns which can be used as a reference for decision making. This paper models the spread of the pandemic with mathematical analysis to provide predictions for different classes of individuals. We consider the spread by using a branching process and a substitution dynamical system as random and deterministic models, respectively, to approximate the pandemic outbreak. Both approaches are based on the assumption of Markov processing. The deterministic model provides an explicit estimate for the proportion of individuals of a certain type in the particular generation given any initial condition, where a generation means a unit of observation time. The proportion relates to the matrix derived from the Markov setting. In addition, the methodology reveals the efficiency of epidemic control policies, such as vaccine injections or quarantine, by the relative spread rate that is used for the prediction of the number of individuals of a certain type. On the other hand, the stochastic approximation has more of an empirical impact than the deterministic one does. Our investigation explicitly exhibits the spread rate of a certain type with respect to an initial condition of any type. After estimating the average spread rate, the effect of adopting a particular policy can be evaluated. The novelty of this elucidation lies in connecting these two models and introducing the idea of the transition spread model between two topological spread models to capture the change of the spread patterns, which is a real-world phenomenon during the epidemic periods due to changes in the environment or changes in disease control policies. Roughly speaking, the deterministic model is a special case of the stochastic model under some particular probability. Most importantly, with the help of the stochastic model, we establish the transition processing of two deterministic models, which is called a transition model. In other words, any stochastic model is "bounded" by two deterministic models. Moreover, a computable way has been established to predict the long-term spread rate due to the Markov properties of the models and matrix representations for the spread patterns. [ABSTRACT FROM AUTHOR]